«Automation and Informatization of the fuel and energy complex»

Machine learning application when solving problems of data preparation automation in the process of system integration implementation

UDC: 004.048
DOI: -

Authors:

¹ National University of Oil and Gas "Gubkin University", Moscow, Russia

Keywords: integration, automation, machine learning, artificial intelligence, information interaction

Annotation:

The authors of the article discuss the problems of systems and applications integration. The main integration problem is divided into two important aspects: preparation with subsequent data processing and technical software implementation of the systems interaction process. In turn, working with data includes various tasks applicable to a specific type of integration, namely tasks of data optimization, comparison, normalization, transformation and others. The authors of the article consider the possibility of using machine learning and artificial intelligence in the tasks of automated data preparation for the integration of systems and applications. Examples of data preparation using modules and libraries of the high-level Python programming language are considered. In future, it is planned to implement a prototype module for automated and customized work with data as well as to study methods for assessing the quality and reliability of integration.

Bibliography:

1. Morozova O.A. Integratsiya korporativnykh informatsionnykh sistem: ucheb. posobie. – M.: Finansovyy un-t pri Pravitel'stve Rossiyskoy Federatsii, 2014. – 140 s.
2. Abramenkova K.N., Tupysev A.M. Kontseptsiya avtogeneratsii programmnogo bloka v zadachakh integratsii informatsionnykh sistem // Avtomatizatsiya i informatizatsiya TEK. – 2023. – № 9(602). – S. 41–45. – DOI: 10.33285/2782-604X-2023-9(602)-41-45
3. Svidetel'stvo o gos. registratsii programmy dlya EVM 2023684080 Ros. Federatsiya. Modul' avtomaticheskoy generatsii programmnogo koda v zadachakh integratsii informatsionnykh sistem / D.G. Leonov, K.N. Telegova; pravoobladatel' FGBOU VO "RGU nefti i gaza im. I.M. Gubkina". – № 2023683632; zayavl. 08.11.2023; opubl. 13.11.2023, Byul. № 11.
4. Python 3.12.6 documentation. – URL: https://docs.python.org/3/ (data obrashcheniya 21.05.2024).
5. Scikit-learn. Machine Learning in Python. – URL: https://scikit-learn.org/stable/index.html (data obrashcheniya 21.05.2024).
6. Feature-engine. – URL: https://feature-engine.trainindata.com/en/latest/ (data obrashcheniya 26.05.2024).
7. Matplotlib. – URL: https://matplotlib.org/ (data obrashcheniya 25.06.2024).
8. Papilina T.M. Platforma razrabotki prikladnykh web-instrumentov dlya dispetcherskogo personala neftegazovoy otrasli // Avtomatizatsiya, telemekhanizatsiya i svyaz' v neftyanoy prom-sti. – 2015. – № 11. – S. 41–46.
9. Leonov D.G. Metody, modeli i tekhnologii razrabotki i integratsii raspredelennykh geterogennykh programmno-vychislitel'nykh kompleksov v transporte gaza. – M.: RGU nefti i gaza (NIU) im. I.M. Gubkina, 2017. – 196 s.